{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46935fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import exp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2671b66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_name():\n",
    "    temp_dataset = pd.read_csv(\"Fungi_temperature_curves.csv\")\n",
    "    temp_data = temp_dataset.values\n",
    "    fungi_name = np.unique(temp_data[:, 0])\n",
    "    return fungi_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c446a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_temp(name_list, temp, temp_dataset):\n",
    "    a = []\n",
    "    for i in range(len(name_list)):\n",
    "        slice = temp_dataset[(temp_dataset['species'] == name_list[i]) &\n",
    "                (temp_dataset['type'] == 'smoothed')]\n",
    "        slice1 = slice[slice['temp_c'] == float(temp)]\n",
    "        a.append(slice1['hyphal_rate'].values[0])\n",
    "    return np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35178949",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_moi(name_list, moi, moi_dataset):\n",
    "    a = []\n",
    "    for i in range(len(name_list)):\n",
    "        slice = moi_dataset[(moi_dataset['species'] == name_list[i]) &\n",
    "                                    (moi_dataset['type'] == 'smoothed')]\n",
    "        a.append(slice[slice['matric_pot'] == float(moi)]['hyphal_rate'].values[0])\n",
    "    return np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88b72b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_moi_max(name_list, moi_dataset):\n",
    "    a = []\n",
    "    for i in range(len(name_list)):\n",
    "        slice = moi_dataset[(moi_dataset['species'] == name_list[i]) &\n",
    "            (moi_dataset['type'] == 'smoothed')]\n",
    "        a.append(np.max(np.array(slice['hyphal_rate'])))\n",
    "    return np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e51a278c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_temp_max(name_list,temp_dataset):\n",
    "    a = []\n",
    "    for i in range(len(name_list)):\n",
    "        slice = temp_dataset[(temp_dataset['species'] == name_list[i]) &\n",
    "            (temp_dataset['type'] == 'smoothed')]\n",
    "        a.append(np.max(np.array(slice['hyphal_rate'])))\n",
    "    return np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "025b511a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hyphal_rate(name_list, moi, temp):\n",
    "    temp_dataset = pd.read_csv(\"Fungi_temperature_curves.csv\")\n",
    "    moi_dataset = pd.read_csv(\"Fungi_moisture_curves.csv\")\n",
    "    temp_max = get_temp_max(name_list, temp_dataset)\n",
    "    moi_max = get_moi_max(name_list, moi_dataset)\n",
    "    moi_hyphal_rate = get_moi(name_list, moi, moi_dataset)\n",
    "    temp_hyphal_rate = get_temp(name_list, temp, temp_dataset)\n",
    "    max_rate = np.array([max(temp_max[i], moi_max[i]) for i in range(len(temp_max))])\n",
    "    a = moi_hyphal_rate/moi_max\n",
    "    b = temp_hyphal_rate/temp_max\n",
    "    hyphal_rate = max_rate*a*b\n",
    "    return hyphal_rate, a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bdd09e62",
   "metadata": {},
   "outputs": [],
   "source": [
    "fungi_names= ['p.flav.s', 'p.har.n', 'm.trem.n', 'p.sang.s', 'h.seti.n']\n",
    "x = np.linspace(0, -5, 501)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ef42c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in fungi_names:\n",
    "    moi_dataset = pd.read_csv(\"Fungi_moisture_curves.csv\")\n",
    "    moi_data = moi_dataset.values\n",
    "    slice = moi_dataset[(moi_dataset['species'] == name) &\n",
    "        (moi_dataset['type'] == 'smoothed')]\n",
    "    y = slice[\"hyphal_rate\"].values\n",
    "    plt.plot(x,y,label=name)\n",
    "    plt.yticks(np.linspace(0, 10, 6), fontsize=12)\n",
    "    plt.xticks(np.linspace(-5, 0, 6), fontsize=12)\n",
    "    plt.ylabel(\"Hyphal extension rate(mm/day)\", fontsize=14)\n",
    "    plt.xlabel(\"Water potential(MPa)\", fontsize=14)\n",
    "    plt.grid(color='grey', linestyle='--', linewidth=1, alpha=0.3)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "x = np.linspace(-5, 50, 5501)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "acd9c8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in fungi_names:\n",
    "    moi_dataset = pd.read_csv(\"Fungi_temperature_curves.csv\")\n",
    "    moi_data = moi_dataset.values\n",
    "    slice = moi_dataset[(moi_dataset['species'] == name) &\n",
    "    (moi_dataset['type'] == 'smoothed')]\n",
    "    y = slice[\"hyphal_rate\"].values\n",
    "    plt.plot(x,y,label=name)\n",
    "    plt.yticks(np.linspace(0, 15, 6), fontsize=12)\n",
    "    plt.xticks(np.linspace(0, 50, 6), fontsize=12)\n",
    "    plt.ylabel(\"Hyphal extension rate(mm/day)\", fontsize=14)\n",
    "    plt.xlabel(\"Temperature()\", fontsize=14)\n",
    "    plt.grid(color='grey', linestyle='--', linewidth=1, alpha=0.3)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4af27495",
   "metadata": {},
   "outputs": [],
   "source": [
    "fungi_names = ['p.flav.s', 'p.har.n', 'm.trem.n', 'p.sang.s', 'h.seti.n']\n",
    "def lineartransform(x):# turn moisture to water potential\n",
    "    y = -5 + x / 20\n",
    "    return format(y, '.2f')\n",
    "def f(hyphal_rate, temperature, factor):# get decomposition rate by interpolation\n",
    "    delta = (2.633 - 1.519) / 120\n",
    "    a = int((temperature - 10) / 0.1)*delta+1.519\n",
    "    y = exp(a) * (hyphal_rate ** (0.44))*factor/100/122\n",
    "    return y\n",
    "def get_m(humidity, temperature, fungi_names):#get the growth-hyphal extension ratio\n",
    "    water_potential = lineartransform(humidity)\n",
    "    m, factor = Get_hyphal_rate.get_hyphal_rate(fungi_names, water_potential,\n",
    "    temperature)\n",
    "# get_hyphal_rate: get hyphal extension rate under certain moisture and temperature\n",
    "    return m, factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21128e44",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataprocess' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10284/2381626495.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_longTH\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt_step\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mhumidity\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mH\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dataprocess' is not defined"
     ]
    }
   ],
   "source": [
    "delta_t, s, t_step = 1, 5, 122\n",
    "F, D, l = np.ones((s, t_step)), np.ones((s, t_step-1)), np.ones((s, t_step))\n",
    "V = np.zeros(t_step)\n",
    "V[0], K = 1, 300\n",
    "F[:, 0] = np.array([1, 25, 5, 15, 10])\n",
    "n = np.array([100, 100, 100, 100, 100])\n",
    "T, H = dataprocess.get_longTH(0)\n",
    "for i in range(t_step-1):\n",
    "    humidity = H[i]\n",
    "    temperature = T[i]\n",
    "    print(i)\n",
    "    m, factor = get_m(humidity, temperature,fungi_names)\n",
    "    r = np.diag(m/5)\n",
    "    c = [1-mi/np.sum(m) for mi in m]\n",
    "    C = np.array([[1, c[0], c[0], c[0], c[0]],\n",
    "    [c[1], 1, c[1], c[1], c[1]],\n",
    "    [c[2], c[2], 1, c[2], c[2]],\n",
    "    [c[3], c[3], c[3], 1, c[3]],\n",
    "    [c[4], c[4], c[4], c[4], 1]])\n",
    "    vector = F[:, i]/n\n",
    "    B = np.diag(np.diag(r*F[:, i]))@C\n",
    "    F[:, i+1] = F[:, i]+(r@F[:, i]-B@vector)*delta_t\n",
    "    l[:, i+1] = l[:, i]+delta_t*(1-(np.sum(F[:, i]))/K)*m.T\n",
    "    D[:, i] = f((1-(np.sum(F[:, i]))/K)*m.T, temperature, factor)\n",
    "V[i+1] = V[i]+(-np.sum(D[:, i]))*delta_t*V[i]*(1-(np.sum(F[:, i]))/K)\n",
    "t = [i*delta_t for i in range(t_step)]\n",
    "plt.plot(t, F[0, :], c='steelblue', label='p.flav.s')\n",
    "plt.plot(t, F[1, :], c='darkorange', label='p.har.n')\n",
    "plt.plot(t, F[2, :], c='forestgreen', label='m.trem.n')\n",
    "plt.plot(t, F[3, :], c='firebrick', label='p.sang.s')\n",
    "plt.plot(t, F[4, :], c='mediumpurple', label='h.seti.n')\n",
    "plt.xlabel(\"Time(day)\", fontsize=16)\n",
    "plt.ylabel(\"The density of fungi\"+\"\"+\"(g/m\"+\"\\u00b2\"+\")\", fontsize=16)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.legend(fontsize=10)\n",
    "plt.show()\n",
    "\n",
    "plt.plot(t, V, c='b', alpha=0.6, linewidth=2)\n",
    "plt.xlabel(\"Time(day)\", fontsize=14)\n",
    "plt.ylabel(\"Relative density of woody fibers\", fontsize=14)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06fabbdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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